Supply-demand-aware deep reinforcement learning for dynamic fleet management
Online ride-hailing platforms have reduced significantly the amounts of the time that taxis are
idle and that passengers spend on waiting. As a key component of these platforms, the fleet …
idle and that passengers spend on waiting. As a key component of these platforms, the fleet …
A robust deep reinforcement learning approach to driverless taxi dispatching under uncertain demand
With the progressive technological advancement of autonomous vehicles, taxi service
providers are expected to offer driverless taxi systems that alleviate traffic congestion and …
providers are expected to offer driverless taxi systems that alleviate traffic congestion and …
Context-aware taxi dispatching at city-scale using deep reinforcement learning
Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among
different locations in a city. Recent advances primarily rely on deep reinforcement learning …
different locations in a city. Recent advances primarily rely on deep reinforcement learning …
MOVI: A model-free approach to dynamic fleet management
T Oda, C Joe-Wong - IEEE INFOCOM 2018-IEEE Conference …, 2018 - ieeexplore.ieee.org
Modern vehicle fleets, eg, for ridesharing platforms and taxi companies, can reduce
passengers' waiting times by proactively dispatching vehicles to locations where pickup …
passengers' waiting times by proactively dispatching vehicles to locations where pickup …
Combinatorial optimization meets reinforcement learning: Effective taxi order dispatching at large-scale
Ride hailing has become prevailing. Central in ride hailing platforms is taxi order
dispatching which involves recommending a suitable driver for each order. Previous works …
dispatching which involves recommending a suitable driver for each order. Previous works …
Deeppool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning
AO Al-Abbasi, A Ghosh… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The success of modern ride-sharing platforms crucially depends on the profit of the ride-
sharing fleet operating companies, and how efficiently the resources are managed. Further …
sharing fleet operating companies, and how efficiently the resources are managed. Further …
An integrated reinforcement learning and centralized programming approach for online taxi dispatching
Balancing the supply and demand for ride-sourcing companies is a challenging issue,
especially with real-time requests and stochastic traffic conditions of large-scale congested …
especially with real-time requests and stochastic traffic conditions of large-scale congested …
Efficient ridesharing dispatch using multi-agent reinforcement learning
O De Lima, H Shah, TS Chu, B Fogelson - arXiv preprint arXiv:2006.10897, 2020 - arxiv.org
With the advent of ride-sharing services, there is a huge increase in the number of people
who rely on them for various needs. Most of the earlier approaches tackling this issue …
who rely on them for various needs. Most of the earlier approaches tackling this issue …
Dynamic fleet management with rewriting deep reinforcement learning
W Zhang, Q Wang, J Li, C Xu - IEEE Access, 2020 - ieeexplore.ieee.org
Inefficient supply-demand matching makes the fleet management a research hotpot in ride-
sharing platforms. With the booming of mobile network services, it is promising to abate the …
sharing platforms. With the booming of mobile network services, it is promising to abate the …
META: A city-wide taxi repositioning framework based on multi-agent reinforcement learning
The popularity of online ride-hailing platforms has made people travel smarter than ever
before. But people still frequently encounter the dilemma of “taxi drivers hunt passengers …
before. But people still frequently encounter the dilemma of “taxi drivers hunt passengers …
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